{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9e2048",
   "metadata": {},
   "source": [
    "# Fine-Tuning ChatGPT for Different Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8ddc61",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we import all the necessary packages and modules for our fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b410a579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pprint\n",
    "import time\n",
    "import argparse\n",
    "from ast import literal_eval\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "import wandb\n",
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "\n",
    "from utils_2 import (\n",
    "    dataset_has_format_errors,\n",
    "    check_token_statistics_and_cost_estimate,\n",
    "    load_dataset_task_prompt_mappings,\n",
    "    write_jsonl,\n",
    ")\n",
    "from utils_1 import task_num_to_task_name, dataset_num_to_dataset_name, plot_count_and_normalized_confusion_matrix, task_to_display_labels\n",
    "\n",
    "# Get the notebook's full path\n",
    "notebook_path = os.getcwd()\n",
    "\n",
    "# Set module_dir to the notebook's directory\n",
    "module_dir = notebook_path\n",
    "\n",
    "#api_key_project = \"\" #MK's Key\n",
    "api_key_project = \"\" #new PRODIGI key\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=api_key_project,  # This is the default and can be omitted\n",
    ")\n",
    "\n",
    "\n",
    "WANDB_PROJECT_NAME = \"chatgpt_annotations_llm_comparison\"\n",
    "MODEL_NAME = 'gpt-4o-mini-2024-07-18'\n",
    "COMPLETION_RETRIES = 50\n",
    "\n",
    "print(notebook_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772bcc76",
   "metadata": {},
   "source": [
    "# Utility Functions\n",
    "These functions provide various utilities for tasks such as data loading, token statistics, and other preprocessing steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7e1cb4",
   "metadata": {},
   "source": [
    "## Configuration Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c79142f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration Variables\n",
    "# Manually set the variables that were previously handled by argparse\n",
    "\n",
    "# Type of task to run inference on\n",
    "task = 1  # Choices: [1]\n",
    "\n",
    "# Dataset to run inference on\n",
    "dataset = 2  # Choices: [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "# Expert Dataset for Second Evaluation\n",
    "dataset_eval = 'x' # Choises: ['x']\n",
    "\n",
    "# Size of the sample to generate\n",
    "sample_size = '250'  # Choices: ['100','250']\n",
    "\n",
    "# Path to the directory to store the generated samples\n",
    "output_dir = '../../annotations/chatGPT/output_fine_tuning/'\n",
    "\n",
    "# Random seed to use\n",
    "seed = 2019\n",
    "\n",
    "# Path to the directory containing the datasets\n",
    "data_dir = '../../annotations/chatGPT/input_fine_tuning/'\n",
    "\n",
    "# Whether to use the full label\n",
    "not_use_full_labels = False\n",
    "\n",
    "# Path to the dataset-task mappings file\n",
    "dataset_task_mappings_fp = os.path.normpath(os.path.join(module_dir, '../../task_mappings', 'dataset_task_mappings.csv'))\n",
    "\n",
    "# Whether to rewrite the dataframe in OpenAI format\n",
    "rewrite_df_in_openai = True\n",
    "\n",
    "# Number of epochs to train the model\n",
    "n_epochs = 5\n",
    "\n",
    "# Batch Size for learning\n",
    "n_batch = 20\n",
    "\n",
    "# Name of the run\n",
    "run_name = 'finetune_chetGPT_hatespeech_ngo'\n",
    "\n",
    "# Temperature to use when generating text\n",
    "temp = 0.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e81ba8",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4ef6fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_label_to_completion(label: str, task_num: int, full_label: bool = True) -> str:\n",
    "    new_label = ''\n",
    "\n",
    "    if task_num == 1:\n",
    "        if full_label:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'HATE SPEECH'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'TOXIC SPEECH'\n",
    "            else:\n",
    "                new_label = 'KEINE HATE SPEECH'\n",
    "            assert new_label in ['HATE SPEECH', 'TOXIC SPEECH', 'KEINE HATE SPEECH']\n",
    "        else:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'A'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'C'\n",
    "            else:\n",
    "                new_label = 'B'\n",
    "            assert new_label in ['A', 'B', 'C']\n",
    "\n",
    "    return new_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "02978fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_and_eval_sets(data_dir: str, dataset_num: int, task_num: int, sample_size: int, dataset_eval:str) \\\n",
    "        -> dict[str, pd.DataFrame]:\n",
    "    datasets = dict()\n",
    "\n",
    "    train_dataset_task_files = glob.glob(os.path.join(data_dir, f'ds_{dataset_num}__task_{task_num}_train_set*.csv'))\n",
    "    eval_set_name = f'ds_{dataset_num}__task_{task_num}_eval_set'\n",
    "    datasets[eval_set_name] = pd.read_csv(os.path.join(data_dir, eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    # Load the additional evaluation dataset specified by dataset_eval\n",
    "    second_eval_set_name = f'ds_{dataset_eval}__task_{task_num}_full_eval'\n",
    "    datasets[second_eval_set_name] = pd.read_csv(os.path.join(data_dir, second_eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    if sample_size == 'all':\n",
    "        train_dfs_ = {fn.strip('.csv'): pd.read_csv(fn, encoding='utf-8') for fn in train_dataset_task_files}\n",
    "        datasets.update(train_dfs_)\n",
    "    else:\n",
    "        train_df_fn = f'ds_{dataset_num}__task_{task_num}_train_set_{sample_size}'\n",
    "        datasets[train_df_fn] = pd.read_csv(os.path.join(data_dir, train_df_fn + '.csv'), encoding='utf-8')\n",
    "\n",
    "        if train_df_fn not in [os.path.basename(fn).strip('.csv') for fn in train_dataset_task_files]:\n",
    "            raise ValueError(f\"Sample size {sample_size} not found for\"\n",
    "                             f\" dataset {dataset_num} and task {task_num}\")\n",
    "\n",
    "    return datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "904e16c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_training_example(system_prompt, user_prompt_format, user_prompt_text, completion):\n",
    "    return {'messages': [\n",
    "        {'role': 'system',\n",
    "         'content': system_prompt},\n",
    "\n",
    "        {'role': 'user',\n",
    "         'content': user_prompt_format.format(text=user_prompt_text)},\n",
    "\n",
    "        {'role': 'assistant',\n",
    "         'content': completion}\n",
    "    ]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "bdf692a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets):\n",
    "    hatespeech_open_ai_metadata = list()\n",
    "\n",
    "    df_id_metadata = pd.DataFrame() if not os.path.exists('hatespeech_open_ai_metadata.csv') \\\n",
    "        else pd.read_csv('hatespeech_open_ai_metadata.csv')\n",
    "\n",
    "    for df_name, df in datasets.items():\n",
    "        df_jsonl_filename = os.path.join(output_dir, 'temp', df_name + '.jsonl')\n",
    "        write_jsonl(data_list=df['openai_instance_format'].tolist(), filename=df_jsonl_filename)\n",
    "\n",
    "        if not_use_full_labels:\n",
    "            df_name += '_single_letter_labels'\n",
    "\n",
    "        if (not rewrite_df_in_openai and\n",
    "                (len(df_id_metadata) > 0 and df_name in df_id_metadata['df_name'].tolist())):\n",
    "            print(f\"Dataset {df_name} already uploaded to OpenAI\")\n",
    "            continue\n",
    "\n",
    "        print(f\"Uploading {df_name} to OpenAI\")\n",
    "        df_response = client.files.create(\n",
    "            file=open(df_jsonl_filename, \"rb\"), purpose=\"fine-tune\"\n",
    "        )\n",
    "        df_response_dict = df_response.to_dict()\n",
    "        df_file_id = df_response_dict[\"id\"]\n",
    "\n",
    "        # Wait until the file is processed\n",
    "        while True:\n",
    "            file = client.files.retrieve(df_file_id)\n",
    "            file_dict = file.to_dict()\n",
    "            if file_dict[\"status\"] == \"processed\":\n",
    "                break\n",
    "            time.sleep(15)\n",
    "        hatespeech_open_ai_metadata.append({'df_name': df_name, 'file_id': df_file_id})\n",
    "\n",
    "    df_id_metadata = pd.concat([df_id_metadata, pd.DataFrame(hatespeech_open_ai_metadata)])\n",
    "    df_id_metadata.to_csv('hatespeech_open_ai_metadata.csv', index=False)\n",
    "\n",
    "    return df_id_metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ace921b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name, n_epochs):\n",
    "    response = client.fine_tuning.jobs.create(\n",
    "        training_file=training_file_id,\n",
    "        validation_file=evaluation_file_id,\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        suffix=model_name,\n",
    "        hyperparameters={\"n_epochs\": n_epochs,\n",
    "                         \"batch_size\": n_batch}\n",
    "    )\n",
    "\n",
    "    response_dict = response.to_dict()\n",
    "    job_id = response_dict[\"id\"]\n",
    "    print(\"Job ID:\", response_dict[\"id\"])\n",
    "    print(\"Status:\", response_dict[\"status\"])\n",
    "\n",
    "    # Wait until the job is done\n",
    "    while True:\n",
    "        job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "        job_dict = job.to_dict()\n",
    "        if job_dict[\"status\"] == \"succeeded\":\n",
    "            break\n",
    "        elif job_dict[\"status\"] == \"failed\":\n",
    "            raise Exception(\"Training failed: %s\" % job_dict[\"error\"])\n",
    "        time.sleep(30)\n",
    "\n",
    "    return job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2026f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_and_log_finetuning_event_history(job_id):\n",
    "    response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id)\n",
    "    response_dict = response.to_dict()\n",
    "    events = response_dict[\"data\"]\n",
    "    events.reverse()\n",
    "    for event in events:\n",
    "        print(event[\"message\"])\n",
    "\n",
    "    # Log events\n",
    "    for event in events:\n",
    "        if event['type'] != 'metrics':\n",
    "            continue\n",
    "        data = event['data']\n",
    "        wandb.log(\n",
    "            {\n",
    "                \"train_loss\": data.get(\"train_loss\"),\n",
    "                \"valid_loss\": data.get(\"valid_loss\"),\n",
    "                \"train_mean_token_accuracy\": data.get(\"train_mean_token_accuracy\"),\n",
    "                \"valid_mean_token_accuracy\": data.get(\"valid_mean_token_accuracy\")\n",
    "            },\n",
    "            step=data.get('step', 0)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc50a97",
   "metadata": {},
   "source": [
    "# Main Implementation\n",
    "This section contains the main code for fine-tuning the ChatGPT model. It includes setup, data preparation, training, and evaluation steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b7246",
   "metadata": {},
   "source": [
    "### Connect to WandB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "37564c84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "wandb version 0.19.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.11"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\\wandb\\run-20250228_121837-kwp5574x</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Syncing run <strong><a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/kwp5574x' target=\"_blank\">finetune_chetGPT_hatespeech_alliance</a></strong> to <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/kwp5574x' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/kwp5574x</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/kwp5574x?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
      ],
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x2abde29c250>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 1/2\n",
    "# Initialize the Weights and Biases run\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=WANDB_PROJECT_NAME,\n",
    "    name=run_name if run_name != '' else f'{MODEL_NAME}_ds_{dataset}_task_{int(task)}'\n",
    "                                                    f'_sample_{sample_size}_epochs_{n_epochs}'\n",
    "                                                    f'_full_label_names_{str(not not_use_full_labels)}'\n",
    "                                                    f'_temp_{temp}',\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config = {\n",
    "        \"model\": MODEL_NAME,\n",
    "        \"dataset\": dataset_num_to_dataset_name[int(dataset)],\n",
    "        \"task\": task_num_to_task_name[int(task)],\n",
    "        \"epochs\": n_epochs,\n",
    "        \"temp\": temp\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f1df25",
   "metadata": {},
   "source": [
    "### Load and Process Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2454b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 3\n",
    "# Load the dataset and filename\n",
    "dataset_idx, dataset_task_mappings = load_dataset_task_prompt_mappings(\n",
    "    dataset_num=dataset, task_num=task, dataset_task_mappings_fp=dataset_task_mappings_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d35437fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 5\n",
    "# Load the train and eval datasets\n",
    "datasets = load_train_and_eval_sets(\n",
    "    data_dir=data_dir, dataset_num=dataset, task_num=task, sample_size=sample_size, dataset_eval=dataset_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9f379906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact prompts>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 4\n",
    "# Get information specific to the dataset\n",
    "label_column = dataset_task_mappings.loc[dataset_idx, \"label_column\"]\n",
    "system_prompt = dataset_task_mappings.loc[dataset_idx, 'zero_shot_prompt']\n",
    "user_prompt_format = dataset_task_mappings.loc[dataset_idx, 'user_prompt']\n",
    "    \n",
    "# Log the system prompt and user_prompt_format as files in wandb\n",
    "prompts_artifact = wandb.Artifact('prompts', type='prompts')\n",
    "with prompts_artifact.new_file('system_prompt.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(system_prompt)\n",
    "with prompts_artifact.new_file('user_prompt_format.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(user_prompt_format)\n",
    "wandb.run.log_artifact(prompts_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "47917349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 6\n",
    "# Generate the training and evaluation examples in the way expected by the Open AI API to finetune chatgpt3.5\n",
    "preprocessed_output_dir = os.path.join(\n",
    "    output_dir, 'preprocessed', 'full_name_labels' if not not_use_full_labels else 'single_letter_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5a9616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 7\n",
    "for df_name, df in datasets.items():\n",
    "    df['completion_label'] = df[label_column].map(\n",
    "        lambda label: map_label_to_completion(label=label, task_num = task,\n",
    "                                              full_label=not not_use_full_labels)\n",
    "        )\n",
    "    df['openai_instance_format'] = df.apply(\n",
    "        lambda row: create_training_example(\n",
    "            system_prompt=system_prompt, user_prompt_format=user_prompt_format,\n",
    "            user_prompt_text=row['text'],\n",
    "            completion=row['completion_label']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    df['openai_instance_without_completion'] = df['openai_instance_format'].map(lambda x: x['messages'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8bcd8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check for errors ds_2__task_1_train_set_250 set: \n",
      "No errors found\n"
     ]
    }
   ],
   "source": [
    "# Section 8\n",
    "print(f'Check for errors {df_name} set: ')\n",
    "assert not dataset_has_format_errors(df['openai_instance_format'].tolist()), f\"Errors found in {df_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9b1d7833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 9\n",
    "df.to_csv(os.path.join(preprocessed_output_dir, df_name + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cdc6b4",
   "metadata": {},
   "source": [
    "### Upload Training Dataset to OPEN AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2fd9b187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading ds_2__task_1_eval_set to OpenAI\n",
      "Uploading ds_x__task_1_full_eval to OpenAI\n",
      "Uploading ds_2__task_1_train_set_250 to OpenAI\n"
     ]
    }
   ],
   "source": [
    "# Section 10\n",
    "# Create jsonl file and upload to OpenAI\n",
    "df_id_metadata =upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "705758da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 11\n",
    "# delete all files in the temp folder\n",
    "os.system(f\"rm -rf {os.path.join(output_dir, 'temp')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4519ab5a",
   "metadata": {},
   "source": [
    "### Finetune chatGPT with the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b0ec6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 12\n",
    "# Run training on the train_df samples selected\n",
    "eval_set_name = f'ds_{dataset}__task_{task}_eval_set'\n",
    "eval_df = datasets[eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a76b1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning ds_2__task_1_train_set_250\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Section 13\n",
    "print(f\"Finetuning {df_name}\")\n",
    "print('-' * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a1036bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 14\n",
    "# Log in wandb.config the dataset sample size used\n",
    "sample_size = df_name.split('_')[-1]\n",
    "wandb.config['trainset_size'] = sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d0860e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the evaluation set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 999\n",
      "mean / median: 880.225888324873, 869.5\n",
      "p5 / p95: 853.0, 921.4\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.629441624365482, 6.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~346809 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1734045 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 15\n",
    "# Check statistics and possible cost for each dataset\n",
    "print('Statistics for the evaluation set: ')\n",
    "eval_tokens = check_token_statistics_and_cost_estimate(\n",
    "    datasets[eval_set_name]['openai_instance_format'].tolist(), target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "254bdfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the training set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 997\n",
      "mean / median: 881.32, 869.0\n",
      "p5 / p95: 853.0, 928.2\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.44, 6.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~220330 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1101650 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 16\n",
    "print(f'Statistics for the training set: ')\n",
    "train_tokens = check_token_statistics_and_cost_estimate(df['openai_instance_format'].tolist(),\n",
    "                                                        target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "91aed7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tokens: 567139\n",
      "\n",
      "#### Estimated cost: 4.54 USD\n"
     ]
    }
   ],
   "source": [
    "# Section 17\n",
    "print(f\"Total number of tokens: {eval_tokens + train_tokens}\\n\")\n",
    "print(f\"#### Estimated cost: {round((eval_tokens + train_tokens) * (0.008 / 1000), 2)} USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5a4cb776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 18\n",
    "# Run the fine-tuning job\n",
    "training_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == df_name, 'file_id'].values[-1]\n",
    "evaluation_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == eval_set_name, 'file_id'].values[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6b0baf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: ftjob-5qSdI9zqUD88w6DwncX4KBwH\n",
      "Status: validating_files\n"
     ]
    }
   ],
   "source": [
    "# Section 19\n",
    "model_name = (df_name.replace('__', '_')\n",
    "              .replace('train_set', 'trn')\n",
    "              .replace('task', 't')\n",
    "              .replace('_single_letter_labels', '_sl'))\n",
    "job_id = fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name=model_name, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6aff8caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model ft:gpt-4o-mini-2024-07-18:university-of-zurich:ds-2-t-1-trn-250:B5t3iS9n has been successfully fine-tuned\n"
     ]
    }
   ],
   "source": [
    "# Section 20\n",
    "# Print the model name\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "response_dict = response.to_dict()\n",
    "full_model_name = response_dict[\"fine_tuned_model\"]\n",
    "print(f'The model {full_model_name} has been successfully fine-tuned')\n",
    "wandb.config['model_name_openai'] = full_model_name\n",
    "wandb.config['finetuning_jobid'] = job_id\n",
    "wandb.config['training_file_openai_id'] = response_dict['training_file']\n",
    "wandb.config['validation_file_openai_id'] = response_dict['validation_file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "45b169bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 48/63: training loss=0.06, validation loss=0.10\n",
      "Step 49/63: training loss=0.03, validation loss=0.06\n",
      "Step 50/63: training loss=0.05, validation loss=0.05\n",
      "Step 51/63: training loss=0.05, validation loss=0.07\n",
      "Step 52/63: training loss=0.01, validation loss=0.04, full validation loss=0.05\n",
      "Step 53/63: training loss=0.02, validation loss=0.02\n",
      "Step 54/63: training loss=0.02, validation loss=0.06\n",
      "Step 55/63: training loss=0.09, validation loss=0.08\n",
      "Step 56/63: training loss=0.03, validation loss=0.13\n",
      "Step 57/63: training loss=0.05, validation loss=0.02\n",
      "Step 58/63: training loss=0.03, validation loss=0.04\n",
      "Step 59/63: training loss=0.03, validation loss=0.02\n",
      "Step 60/63: training loss=0.01, validation loss=0.03\n",
      "Step 61/63: training loss=0.01, validation loss=0.12\n",
      "Step 62/63: training loss=0.03, validation loss=0.16\n",
      "Step 63/63: training loss=0.00, validation loss=0.05, full validation loss=0.06\n",
      "Checkpoint created at step 39\n",
      "Checkpoint created at step 52\n",
      "New fine-tuned model created\n",
      "The job has successfully completed\n"
     ]
    }
   ],
   "source": [
    "# Section 21\n",
    "# Print the events (training history of the model)\n",
    "print_and_log_finetuning_event_history(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c579b37",
   "metadata": {},
   "source": [
    "## Evaluate Model on Validation Set with Annotations of the Group from which the Training Data is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "fba537b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Section 22\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "0a544a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 394/394 [03:39<00:00,  1.80it/s]\n"
     ]
    }
   ],
   "source": [
    "# Section 23\n",
    "for messages in tqdm(eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "337b13ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 24\n",
    "# Add predictions to df\n",
    "eval_df['prediction'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fe242636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 25\n",
    "# Store output\n",
    "predictions_output_dir = os.path.join(output_dir, 'predictions',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir, exist_ok=True)\n",
    "datasets[eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "bcaddde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 26\n",
    "# Get performance metrics\n",
    "y_true = eval_df['completion_label']\n",
    "y_pred = eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3d085eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 27\n",
    "label_type = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels = task_to_display_labels[task][label_type]\n",
    "labels = display_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a1ce1492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.7272727272727272,\n",
      "                 'precision': 0.6666666666666666,\n",
      "                 'recall': 0.8,\n",
      "                 'support': 60},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.9320066334991708,\n",
      "                       'precision': 0.9525423728813559,\n",
      "                       'recall': 0.9123376623376623,\n",
      "                       'support': 308},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.7169811320754716,\n",
      "                  'precision': 0.7037037037037037,\n",
      "                  'recall': 0.7307692307692307,\n",
      "                  'support': 26},\n",
      " 'accuracy': 0.883248730964467,\n",
      " 'macro avg': {'f1-score': 0.7920868309491231,\n",
      "               'precision': 0.7743042477505755,\n",
      "               'recall': 0.8143689643689643,\n",
      "               'support': 394},\n",
      " 'weighted avg': {'f1-score': 0.8866393811879961,\n",
      "                  'precision': 0.8925871754917613,\n",
      "                  'recall': 0.883248730964467,\n",
      "                  'support': 394}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 28\n",
    "cm_plot, classification_report, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true, y_pred, display_labels, labels, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "75c64dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 29\n",
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "4aaa4209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 31\n",
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "45aaca05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_ds_2_t_1_trn_250>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 32\n",
    "# Log the classification report as an artifact\n",
    "classification_report = (pd.DataFrame({k: v for k, v in classification_report.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report': wandb.Table(\n",
    "    dataframe=classification_report)})\n",
    "\n",
    "classification_report_artifact = wandb.Artifact(\n",
    "      f'classification_report_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact.new_file('classification_report.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db7bbf2",
   "metadata": {},
   "source": [
    "## Evaluation on Second Evaluation Set\n",
    "In this section, we will evaluate the performance of our model on a second evaluation set (`second_eval_set`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8cbb7ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ds_2__task_1_eval_set', 'ds_x__task_1_full_eval', 'ds_2__task_1_train_set_250'])\n"
     ]
    }
   ],
   "source": [
    "print(datasets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_eval_set_name = f'ds_{dataset_eval}__task_{task}_full_eval'\n",
    "full_eval_df = datasets[second_eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == second_eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "9cfc903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_eval_df = datasets[second_eval_set_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "303aeed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Load the second evaluation set (second_eval_set)\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions_2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7c218926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494/494 [04:47<00:00,  1.72it/s]\n"
     ]
    }
   ],
   "source": [
    "for messages in tqdm(full_eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions_2.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "789fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add predictions to df\n",
    "full_eval_df['prediction'] = predictions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "616c4658",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_output_dir_2 = os.path.join(output_dir, 'predictions_2',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir_2, exist_ok=True)\n",
    "datasets[second_eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir_2, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "ec8481ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get performance metrics\n",
    "y_true_2 = full_eval_df['completion_label']\n",
    "y_pred_2 = full_eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "296deb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_type_2 = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels_2 = task_to_display_labels[task][label_type]\n",
    "labels_2 = display_labels_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "83dbd847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.5943775100401606,\n",
      "                 'precision': 0.7047619047619048,\n",
      "                 'recall': 0.5138888888888888,\n",
      "                 'support': 144},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.7946577629382304,\n",
      "                       'precision': 0.6938775510204082,\n",
      "                       'recall': 0.9296875,\n",
      "                       'support': 256},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.37142857142857144,\n",
      "                  'precision': 0.5652173913043478,\n",
      "                  'recall': 0.2765957446808511,\n",
      "                  'support': 94},\n",
      " 'accuracy': 0.6842105263157895,\n",
      " 'macro avg': {'f1-score': 0.5868212814689875,\n",
      "               'precision': 0.6546189490288868,\n",
      "               'recall': 0.5733907111899134,\n",
      "               'support': 494},\n",
      " 'weighted avg': {'f1-score': 0.6557429847616515,\n",
      "                  'precision': 0.6725684253634564,\n",
      "                  'recall': 0.6842105263157895,\n",
      "                  'support': 494}}\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_plot, classification_report_2, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true_2, y_pred_2, display_labels_2, labels_2, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "20289ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "09a5abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix_2': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2399e29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_2_ds_2_t_1_trn_250>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log the classification report as an artifact\n",
    "classification_report_2 = (pd.DataFrame({k: v for k, v in classification_report_2.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report_2': wandb.Table(\n",
    "    dataframe=classification_report_2)})\n",
    "\n",
    "classification_report_artifact_2 = wandb.Artifact(\n",
    "      f'classification_report_2_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact_2.new_file('classification_report_2.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report_2))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b0aa2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>█▁</td></tr><tr><td>f1</td><td>█▁</td></tr><tr><td>precision</td><td>█▁</td></tr><tr><td>recall</td><td>█▁</td></tr><tr><td>train_loss</td><td>▅▃▄▄▁▂▃█▃▄▃▃▂▁▃▁</td></tr><tr><td>train_mean_token_accuracy</td><td>▃▅▄▅██▇▁▅▄▇▅██▄█</td></tr><tr><td>valid_loss</td><td>▅▃▃▃▂▁▃▄▆▁▂▁▁▆█▃</td></tr><tr><td>valid_mean_token_accuracy</td><td>▂▅▅▄▆▇▇▇▂█▇█▇▁▅█</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.68421</td></tr><tr><td>f1</td><td>0.68421</td></tr><tr><td>precision</td><td>0.68421</td></tr><tr><td>recall</td><td>0.68421</td></tr><tr><td>train_loss</td><td>0.00475</td></tr><tr><td>train_mean_token_accuracy</td><td>1.0</td></tr><tr><td>valid_loss</td><td>0.05274</td></tr><tr><td>valid_mean_token_accuracy</td><td>0.99359</td></tr></table><br/></div></div>"
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